The apelin‑apelin receptor signaling pathway in fibroblasts is involved in tumor growth via p53 expression of cancer cells

Cancer-associated fibroblasts (CAFs) are pivotal in tumor progression. TP53-deficiency in cancer cells is associated with robust stromal activation. The apelin-apelin receptor (APJ) system has been implicated in suppressing fibroblast-to-myofibroblast transition in non-neoplastic organ fibrosis. The present study aimed to elucidate the oncogenic role of the apelin-APJ system in tumor fibroblasts. APJ expression and the effect of APJ suppression in fibroblasts were investigated for p53 status in cancer cells using human cell lines (TP53-wild colon cancer, HCT116, and Caco-2; TP53-mutant colon cancer, SW480, and DLD-1; and colon fibroblasts, CCD-18Co), resected human tissue samples of colorectal cancers, and immune-deficient nude mouse xenograft models. The role of exosomes collected by ultracentrifugation were also analyzed as mediators of p53 expression in cancer cells and APJ expression in fibroblasts. APJ expression in fibroblasts co-cultured with p53-suppressed colon cancer cells (HCT116sh p53 cells) was significantly lower than in control colon cancer cells (HCT116sh control cells). APJ-suppressed fibroblasts treated with an antagonist or small interfering RNA showed myofibroblast-like properties, including increased proliferation and migratory abilities, via accelerated phosphorylation of Sma- and Mad-related protein 2/3 (Smad2/3). In addition, xenografts of HCT116 cells with APJ-suppressed fibroblasts showed accelerated tumor growth. By contrast, apelin suppressed the upregulation of phosphorylated Smad2/3 in fibroblasts. MicroRNA 5703 enriched in exosomes derived from HCT116sh p53 cells inhibited APJ expression, and inhibition of miR-5703 diminished APJ suppression in fibroblasts caused by cancer cells. APJ suppression from a specific microRNA in cancer cell-derived exosomes induced CAF-like properties in fibroblasts. Thus, the APJ system in fibroblasts in the tumor microenvironment may be a promising therapeutic target.Saiki H., Hayashi Y., Yoshii S., et al. The apelin‑apelin receptor signaling pathway in fibroblasts is involved in tumor growth via p53 expression of cancer cells. International Journal of Oncology 63, 139 (2023); https://doi.org/10.3892/ijo.2023.5587

A novel artificial intelligence-based endoscopic ultrasonography diagnostic system for diagnosing the invasion depth of early gastric cancer

The version of record of this article, first published in Journal of Gastroenterology, is available online at Publisher’s website: https://doi.org/10.1007/s00535-024-02102-1.Background: We developed an artificial intelligence (AI)-based endoscopic ultrasonography (EUS) system for diagnosing the invasion depth of early gastric cancer (EGC), and we evaluated the performance of this system. Methods: A total of 8280 EUS images from 559 EGC cases were collected from 11 institutions. Within this dataset, 3451 images (285 cases) from one institution were used as a development dataset. The AI model consisted of segmentation and classification steps, followed by the CycleGAN method to bridge differences in EUS images captured by different equipment. AI model performance was evaluated using an internal validation dataset collected from the same institution as the development dataset (1726 images, 135 cases). External validation was conducted using images collected from the other 10 institutions (3103 images, 139 cases). Results: The area under the curve (AUC) of the AI model in the internal validation dataset was 0.870 (95% CI: 0.796–0.944). Regarding diagnostic performance, the accuracy/sensitivity/specificity values of the AI model, experts (n = 6), and nonexperts (n = 8) were 82.2/63.4/90.4%, 81.9/66.3/88.7%, and 68.3/60.9/71.5%, respectively. The AUC of the AI model in the external validation dataset was 0.815 (95% CI: 0.743–0.886). The accuracy/sensitivity/specificity values of the AI model (74.1/73.1/75.0%) and the real-time diagnoses of experts (75.5/79.1/72.2%) in the external validation dataset were comparable. Conclusions: Our AI model demonstrated a diagnostic performance equivalent to that of experts

A machine learning model for predicting the lymph node metastasis of early gastric cancer not meeting the endoscopic curability criteria

The version of record of this article, first published in Gastric Cancer, is available online at Publisher’s website: https://doi.org/10.1007/s10120-024-01511-8.Background: We developed a machine learning (ML) model to predict the risk of lymph node metastasis (LNM) in patients with early gastric cancer (EGC) who did not meet the existing Japanese endoscopic curability criteria and compared its performance with that of the most common clinical risk scoring system, the eCura system. Methods: We used data from 4,042 consecutive patients with EGC from 21 institutions who underwent endoscopic submucosal dissection (ESD) and/or surgery between 2010 and 2021. All resected EGCs were histologically confirmed not to satisfy the current Japanese endoscopic curability criteria. Of all patients, 3,506 constituted the training cohort to develop the neural network-based ML model, and 536 constituted the validation cohort. The performance of our ML model, as measured by the area under the receiver operating characteristic curve (AUC), was compared with that of the eCura system in the validation cohort. Results: LNM rates were 14% (503/3,506) and 7% (39/536) in the training and validation cohorts, respectively. The ML model identified patients with LNM with an AUC of 0.83 (95% confidence interval, 0.76–0.89) in the validation cohort, while the eCura system identified patients with LNM with an AUC of 0.77 (95% confidence interval, 0.70–0.85) (P = 0.006, DeLong’s test). Conclusions: Our ML model performed better than the eCura system for predicting LNM risk in patients with EGC who did not meet the existing Japanese endoscopic curability criteria. Mini-abstract: We developed a neural network-based machine learning model that predicts the risk of lymph node metastasis in patients with early gastric cancer who did not meet the endoscopic curability criteria

スイヘイ オヨビ エンチョク ジシンドウ オ ウケル ヒラゾコ エントウ チョソウ ノ カツドウ ハンテイシキ

本論文では, 水平及び鉛直地震加速度を受けるアンカー構造を有し, かつ底板が浮上らない平底円筒貯槽の滑動の運動方程式を示し, 加速度応答倍率を適用して滑動の判定を行う方法について検討を行った. ここで, 加速度応答倍率の適用は滑動現象にとって最も重要な水平及び鉛直加速度とそれらの応答の同時性を無視することになるので, この点を補うために2つの係数を定義し, 水平及び鉛直加速度とそれらの応答の同時性の効果を滑動判定式に取入れるようにした. そして, それら係数が正規分布で近似される統計的性質を有することを明らかにし, 許容超過確率に基づいて算定した鉛直地震加速度と貯槽の鉛直応答加速度を設計値として用いて滑動の判定ができることを示した. This paper presents the slip verification method for the tank with the seismic magnification factors of both horizontal and vertical directions. The equation is derived from an analytical model for the tank slip including the effects of anchor straps and the responses to the ground acceleration in both directions. Since an application of the seismic magnification factors for the tank slip verification implicitly neglects the coincidence between the ground acceleration and responses to them which considerably affect tank slip behavior, two coefficients which express their coincidence are determined to compensate for the deficiency. Since relationships of the coincidence to their probability of occurrence are clarified, the design vertical ground acceleration and vertical response acceleration based on the allowable probability of excess are introduced to the slip verification method. The proposed method accurately approximates the slip commencement of the tank

The role of CaMKII-Tiam1 complex on learning and memory

International audienc

25 mg versus 50 mg dose of rectal diclofenac for prevention of post-ERCP pancreatitis in Japanese patients: A retrospective study

Yoshihara T, Horimoto M, Kitamura T, et al. 25 mg versus 50 mg dose of rectal diclofenac for prevention of post-ERCP pancreatitis in Japanese patients: a retrospective study. BMJ Open 2015;5:e006950. doi: 10.1136/bmjopen-2014-00695